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Predictive Analytics Using Text Classification for Restaurant Inspections

Authors:

Booma Sowkarthiga Balasubramani,

Isabel F. CruzAuthors Info & Claims

UrbanGIS'17: Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

Article No.: 14, Pages 1 - 4

https://doi.org/10.1145/3152178.3152192

Published: 07 November 2017 Publication History

Abstract

According to the Center for Disease Control (CDC), there are almost 48 million people affected by foodborne diseases in the U.S. every year, including 3,000 deaths. The most effective way of avoiding food poisoning would be its prevention. However, complete prevention is not possible, therefore Public Health departments perform routine restaurant inspections, combined with the practice of inspecting specific restaurants once a disease outbreak is identified. Following other health applications (e.g., prediction of a flu outbreak using Twitter), we use social media and a predictive analytics approach to identify the need for targeted visits by city inspectors.

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Cited By

Chowanda ASutoyo RMeiliana Tanachutiwat S(2021)Exploring Text-based Emotions Recognition Machine Learning Techniques on Social Media ConversationProcedia Computer Science10.1016/j.procs.2021.01.099179(821-828)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.01.099
Wang ZCruz I(2020)Analysis of the Impact of COVID-19 on Education Based on Geotagged TwitterProceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-1910.1145/3423459.3430756(15-23)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3423459.3430756
de Lima Ade Sousa Lima Rda Hora H(2020)Conceptual Model of Professional Supervision Study Based on Data Mining: A Study in the Regional Council of Nutritionists of the 4th Brazilian Region (Rio de Janeiro and Espirito Santo States)Advances in Multidisciplinary Medical Technologies ─ Engineering, Modeling and Findings10.1007/978-3-030-57552-6_2(11-27)Online publication date: 8-Nov-2020
https://doi.org/10.1007/978-3-030-57552-6_2
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Predictive Analytics Using Text Classification for Restaurant Inspections

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Published In

cover image ACM Conferences

UrbanGIS'17: Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

November 2017

118 pages

ISBN:9781450354950

DOI:10.1145/3152178

Copyright © 2017 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 07 November 2017

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SIGSPATIAL'17

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SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 7 - 10, 2017

CA, Redondo Beach, USA

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Cited By

Chowanda ASutoyo RMeiliana Tanachutiwat S(2021)Exploring Text-based Emotions Recognition Machine Learning Techniques on Social Media ConversationProcedia Computer Science10.1016/j.procs.2021.01.099179(821-828)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.01.099
Wang ZCruz I(2020)Analysis of the Impact of COVID-19 on Education Based on Geotagged TwitterProceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-1910.1145/3423459.3430756(15-23)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3423459.3430756
de Lima Ade Sousa Lima Rda Hora H(2020)Conceptual Model of Professional Supervision Study Based on Data Mining: A Study in the Regional Council of Nutritionists of the 4th Brazilian Region (Rio de Janeiro and Espirito Santo States)Advances in Multidisciplinary Medical Technologies ─ Engineering, Modeling and Findings10.1007/978-3-030-57552-6_2(11-27)Online publication date: 8-Nov-2020
https://doi.org/10.1007/978-3-030-57552-6_2
Shivaprabhu VBalasubramani BCruz I(2017)Ontology-based Instance Matching for Geospatial Urban Data IntegrationProceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics10.1145/3152178.3152186(1-8)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3152178.3152186

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